Automated methods and systems for analyzing data associated with an industrial process

ABSTRACT

Automated methods and systems for analyzing data associated with an industrial process parse data entries received from devices in an industrial process, such as a mail or paper processing system. The parsing can include applying state machines to the data to identify events and produce output relating to the process for which the data is being analyzed. Statistical measures are computed for the output from each of the state machines. The statistical measures are compared to design limits. Output is displayed to the user in a format that facilitates interpretation of the data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of U.S. patent application Ser. No.09/434,406, filed Nov. 4, 1999 and issued Sep. 23, 2003 as U.S. Pat. No.6,625,567, incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present invention relates to methods and systems for analyzing datarelating to events occurring in industrial processes. More particularly,the present invention relates to automated methods and systems foranalyzing time-tagged data associated with events occurring inindustrial processes.

BACKGROUND ART

Time-tagged data may be used to record events that occur in industrialprocesses. For example, in paper or sheet article processing, such asmail processing, machines such as, for example, inserters, turnoversequencers, accumulators, folders, and collectors include opticalsensors that monitor the flow of sheet articles through the machines.The sheet articles can also include individual or stacked, folded orunfolded sheet articles such as envelopes, envelope inserts and othersuitable sheet articles.

Each sheet article processing machine can comprise a processor, a memorybuffer, and a communications circuit, all of which can cooperate toproduce time-tagged data for a machine. The optical sensors can be usedto detect events, such as the presence of a sheet of paper. Theprocessing circuit receives the data from each of the sensors andassociates a time value with the output of each sensor. The processingcircuit can also convert the output into a code or text stringindicative of the event detected by each sensor. The combination of atime value and a code or text string indicative of an event thatoccurred in an industrial process is referred to herein as “time-taggeddata”. The communication circuits of each of the machines transmit thetime-tagged data to a central location for storage.

The central location can be a suitable computer that communicates witheach of the machines, e.g., using a serial interface, to receive thetime-tagged data from the machines. The time-tagged data can be storedas a log file in a bulk storage medium, such as a hard disk, at thecentral location. Each line of data in the log file is referred to as anentry. Each entry contains one unit of time-tagged data, i.e., one timetag and one event portion. The entries in the log file are analyzedmanually by a technician or an engineer to identify problems associatedwith the industrial process.

One problem with this method of recording and analyzing data regardingan industrial process is that time-tagged data is difficult tointerpret. For example, because time-tagged data is output from multiplesensors on multiple machines or multiple parts of the same machine, andbecause many events can occur simultaneously, no clear sequence oftime-tagged data relating to a single event appears in the log file.Entries recorded by a single sensor can be interspersed with otherentries in the log file. In addition, the text or codes associated witheach event might not readily convey to the observer the nature of theevent. As a result, skilled technicians or even engineers can berequired to analyze the time-tagged data. Because of the complex natureof the time-tagged data, extra labor can be required even for skilledpersons to interpret the time-tagged data.

The following lines of text are an example of time-tagged data recordedin a log file for a paper processing operation:

0000.013977 00.465718 ??.?????? BIN_MUX_IN CLR-board=0,port=06, pin=000

0000.014343 00.465718 ??.?????? BIN_MUX_IN CLR-board=0,port=06,pin=000

0000.027557 00.465718 ??.?????? BIN_MUX_IN SET-board=0,port=05, pin=000

0000.031738 00.465718 ??.?????? BIN_MUX_IN CLR-board=0,port=06, pin=000

0000.033447 00.465718 ??.?????? HTA Variable-WRITE: I#3219,val=65535/Oxffff

0000.033569 00.465718 ??.?????? HTA Page Data-INSIDE:s#=16,p#=2,tg=73,ct=1

0000.033661 00.000091 00.000091 FED_EOS-HTA Page Data:1ST_SUBSETFOLD_LIMIT

0000.061371 00.465718 ??.?????? BIN_MUX_IN SET-board=0,port=09,pin=000

0000.061798 00.465718 ??.?????? BIN_MUX_IN SET-board=0,port=09,pin=000

0000.062683 00.465718 ??.?????? HTA Variable-WRITE: I#2419, val=8/0x0008

In each log file entry, the numbers on the left side indicate the eventoccurrence time in milliseconds measured from a predetermined starttime. The text on the right side of each entry indicates the type ofevent, the name of the sensor that detected the event, and variablevalues associated with the event. As can be seen from the data above, itcan be difficult to determine a real-world event from the event portionof each entry. The difficulty is increased when an event of interestspans multiple separated time-tagged data entries.

In light of these difficulties, there exists a long-felt need forimproved methods and systems for analyzing time-tagged data, identifyingevents in an industrial process based on the time-tagged data, andpresenting the data in a format that is easily understood.

DISCLOSURE OF THE INVENTION

The present invention includes automated methods and systems foranalyzing time-tagged data. The phrase “time-tagged data”, as usedherein, refers to any data associated with an industrial process thathas a time value or time-tag indicating when an event occurred and thathas an event portion that indicates the nature of an event. The methodsand systems according to the present invention analyze the time-taggeddata by applying computer-implemented state machines to time-tagged dataentries to produce data indicative of real-world events that occur in anindustrial process. Statistical measures are computed for the dataoutput from the state machines, and the statistical measures arecompared to design limits. The statistical measures and the results ofcomparing the statistical measures to the design limits are presented toa user in a format that facilitates interpretation of the time-taggeddata.

According to another aspect, the present invention includes methods andsystems for analyzing non-time-tagged data associated with an industrialprocess and presenting output in a manner that facilitates understandingof the data.

Accordingly, it is an object of the present invention to provide novelautomated methods and systems for analyzing time-tagged data.

It is another object of the invention to provide methods and systems foranalyzing time-tagged data to produce output that facilitatesinterpretation of the time-tagged data.

It is another object of the invention to provide methods and systems foranalyzing non-time-tagged data associated with an industrial process.

These objects and others are met in whole or in part by the presentinvention. Some of the objects of the invention having been statedhereinabove, other objects will become evident as the descriptionproceeds, when taken in connection with the accompanying drawings, asbest described hereinbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

A description of the present invention will now proceed with referenceto the accompanying drawings of which:

FIG. 1 is a flow chart illustrating exemplary steps that can beperformed by an automated system for analyzing time-tagged dataaccording to an embodiment of the present invention;

FIG. 2 is a flow chart illustrating a parser for parsing time-taggeddata according to an embodiment of the present invention;

FIG. 3 is a state diagram of a state machine for identifying startingand ending events associated with an industrial process according to anembodiment of the present invention;

FIG. 4 is a state diagram of a state machine for identifying anoverlapped page event in paper processing according to an embodiment ofthe present invention;

FIG. 5 is a process flow diagram of a paper processing system in whichembodiments of the present invention can be used to analyze time-taggeddata;

FIG. 6( a) is a computer-generated image illustrating an output windowfor displaying results of analyzing time-tagged data to a user accordingto an embodiment of the present invention;

FIG. 6( b) is a computer-generated image illustrating a dimensionsdialog box for receiving paper dimensions from a user according to anembodiment of the present invention;

FIG. 6( c) is a computer-generated image illustrating a processinformation dialog box for receiving process information from a useraccording to an embodiment of the present invention; and

FIG. 6( d) is a computer-generated image of a report indicating resultsof analyzing time-tagged data according to an embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

In accordance with the present invention, novel automated methods andsystems for analyzing time-tagged data and non-time-tagged data areprovided. The automated methods and systems for analyzing time-taggeddata and non-time-tagged data according to the present invention will beexplained in the context of flow charts and state diagrams. It isunderstood according to this invention that the flow charts and thestate diagrams can be implemented in hardware, software, or acombination of hardware and software. Thus, the present invention caninclude computer program products comprising computer-executableinstructions embodied in computer-readable media for performing thesteps illustrated in each of the flow charts or implementing the statemachines illustrated in each of the state diagrams.

FIG. 1 is a flow chart illustrating steps that can be performed by anautomated system for analyzing time-tagged data according to anembodiment of the present invention. In step ST1, the system parsestime-tagged data to identify events and measure parameters of interest.In step ST2, the system computes statistical measures, such as meanvalues, minimum values, maximum values, standard deviations, modes,medians, and variances, of parameters measured during the parsing of thetime-tagged data. In step ST3, the system applies limits to thestatistical measures data. The limits can be used to identify whetherthe machine or machines is or are operating within acceptable designtolerances. Limits can vary by type and value for each measuredparameter. In step ST4, the system presents or displays output to auser. The output can include the statistical measures and the results ofcomparing statistical measures to the design limits. If the statisticalmeasures fall outside of the design limits, the user can be alertedthrough any suitable means, such as through display of an alert messageon a computer display device. The system may also warn the user if themeasures are close to but do not exceed a design limit. Warning the userwhen operating conditions are close to but not in excess of designlimits allows the user to identify a problem before a failure occurs.Each of the steps illustrated in FIG. 1 will now be discussed in furtherdetail.

FIG. 2 illustrates an exemplary parser for parsing time-tagged dataaccording to an embodiment of the present invention. In step ST1, theparser reads one entry of the time-tagged data. For example, the parsercan access a log file containing the time-tagged data and read the firstentry from the log file. The parser can execute on the same computer oron a different computer than the computer that stores the log file. Ifthe parser executes on the same computer, the parser can access thememory location containing the log file to read the time-tagged data. Ifthe parser resides on a remote computer, the parser can communicate withthe computer storing the log file, in order to access the time-taggeddata.

The present invention is not limited to analyzing time-tagged datastored in a log file. For example, in an alternative embodiment of thepresent invention, the parser can receive the time-tagged data in realtime from machines performing an industrial process. In such anembodiment, each unit of time tagged data, can be received, processed,and either stored for further analysis or discarded.

According to an important aspect of the invention, as the parser readsthe time tagged data, the parser preferably applies one or more statemachines to the time-tagged data to identify events of interest. Forexample, in step ST2 of FIG. 2, the parser applies a first state machineto one entry of the time-tagged data. The first state machine cananalyze the data to identify an event or events occurring in anindustrial process and produce output relating to the event or events.In step ST3, the parser determines whether all of the state machineshave been executed. If all of the state machines have been executed, theparser determines whether all of the time-tagged data has been analyzed(step ST4). If all of the data has been analyzed, the parser ends, andthe program computes statistical measures for the data produced by thestate machines (Step ST2 in FIG. 1).

In step ST3 of FIG. 2, if all of the state machines have not beenexecuted, the parser preferably applies the next state machine to thecurrent time-tagged data entry (Steps ST4 and ST2). Thus, according toone embodiment of the invention, a series of state machines are appliedto each time-tagged data entry before proceeding to the next time-taggeddata entry. In an alternative embodiment of the present invention, eachstate machine can be applied to all of the time-tagged data receivedand/or stored in a log file before applying the next state machine.However, this embodiment is less desirable than applying all statemachines to each entry before proceeding to the next entry because somestate machines require data produced by other state machines. Thedifficulty in communicating data among state machines is increased whenstate machines are applied in full before applying subsequent statemachines. Once all of the state machines have been applied to all of thedata, control returns to step ST2 in FIG. 1 where statistical measuresare computed for the output produced by application of the statemachines to the time-tagged data.

FIG. 3 illustrates an exemplary state machine that can be applied totime-tagged data in order to measure the time between starting andending events associated with an industrial process. For example, inpaper processing, the starting event can be the activation of a vacuumsolenoid of a sheet feeder. The ending event can be the detection of oneor both edges of a sheet of paper by a sensor, such as a mechanicalsensor, an optical sensor, a magnetic sensor, or an electrical sensor,associated with the sheet feeder. The following event list illustrates,without limitation, the exemplary events that can be monitored by thestate machine illustrated in FIG. 3:

Event List

-   -   A) Vacuum solenoid activated    -   B) System error detected    -   C) Motor power disabled    -   D) Lead edge of paper detected at photocell    -   E) Finished making output.        In the event list, events A and D are the starting and ending        events for which elapsed time can be measured. Events B, C, and        E are other events that can cause transitions between states.

The state machine illustrated in FIG. 3 includes a plurality of statesassociated with the industrial process being monitored. In theillustrated embodiment, the states are:

-   -   S1: Waiting for Starting Event;    -   S2: Waiting for Ending Event; and    -   S3: Generating Output.        The arrows between the states in the state diagram represent        state transitions. The events or state machine inputs that cause        the transitions are indicated by letters A–E beside each arrow.        The operation of the parser in applying the state machine to a        log file containing multiple entries of time-tagged data will        next be explained in detail.

In state S1, the state machine “waits” for a starting event to occur.Waiting for a starting event to occur can include analyzing log fileentries until a log file entry corresponding to the event or events ofinterest is located. As discussed above with respect to FIG. 2, theparser can apply multiple state machines to each log file entry beforeproceeding to the next log file entry. Thus, for state Si of the statemachine illustrated in FIG. 3, the parser determines whether the currentlog file entry corresponds to event A, “Vacuum solenoid activated”. Inorder to determine whether the current log file entry indicates theevent of interest, the parser can apply a pattern matching algorithm tothe event portion of the entry. The pattern matching algorithm can beany suitable algorithm for matching the text string or code in the eventportion of the entry with a known text string or code corresponding tothe event of interest. For example, the pattern matching algorithm canbe similar to or the same as pattern matching algorithms used inInternet search engines or search programs of the Unix operating system.

An example of a time-tagged data entry that identifies the startingevent or activation of a solenoid is as follows:

Event A: Solenoid Activation

-   0002.752235 00.000274 00.095429 SFVACSOL-BIN_OUT CLR:    board=0,port=11,pin=000    As indicated by the data entry, the real-world event corresponding    to the entry might not be readily identifiable to a human observer    without knowledge of the meaning of text strings associated with    real-world events. The parser reduces the need for users to be    familiar with log file data formats by automatically identifying    events of interest from the time-tagged data units.

In the illustrated embodiment, if the parser determines that the eventportion of the entry corresponds to event A, “Vacuum solenoidactivated”, the state machine transitions to state S2 “Waiting forEnding Event”. If the parser determines that the current log file entrybeing analyzed does not correspond to event A, then the state machineremains in state S1. Thus, the state machine automatically filters outirrelevant events, such as events from other sensors or other machines,by only changing states when the event or events of interest occur.

Once the state machine transitions to state S2 and any remaining statemachines have been applied to the current entry, the parser examines thenext data entry for events relevant to state S2. In the illustratedembodiment, the parser determines whether the event portion of the nextdata entry corresponds to event B, “System error detected”, event C,“Motor power disabled, or event D “Lead edge of paper detected atphotocell”. If the parser determines that the event portion of thetime-tagged data entry corresponds to event D, the state machinetransitions to state S3.

The following is an exemplary time-tagged data entry corresponding tothe detection of a sheet of paper at the solenoid:

Event D: Sheet Detected

-   0002.784431 00.004516 00.035156 SFENTCEL-BIN_MUX_IN SET:    board=0,port=06,pin=000    In a complex industrial process, such as paper processing, many    events can occur between the activation of a solenoid and the    detection of a first sheet of paper at the solenoid. For example,    other solenoids can be activated and paper can be detected by other    sensors. These other non-relevant events result in separation    between the entries corresponding to events A an D in FIG. 3. As a    result, a human analyzing the time-tagged data would be required to    expend much time and effort searching the log file for events A    and D. However, because the parser according to the present    embodiment implements one or more state machines that automatically    identify entries and sequences of entries corresponding to events of    interest, the time required for analyzing time-tagged data is    reduced, even when entries are interspersed with other non-relevant    entries.

While in state S2, if the parser determines that the current entrycorresponds to event B, “System error detected” or event C, “Motor powerdisabled”, the state machine returns to state S1. The following entriescorrespond to events B and C:

Event B: System Error Detected

-   0007.335153 00.002685 04.211392 BASE-Error SET: SET Error=0x8251

Event C: Motor Power Disabled

-   0040.408212 00.000274 23.874536 SFMOTPWR-BIN_OUT CLR:    board=0,port=12,pin=000    Thus, in state S2, the parser is required to check the current entry    for multiple events. If the state machine returns to state S1, the    parser checks the next data entry for event A, as described above.

In state S3, the state machine generates output corresponding to theparameter being measured. In the present example, the parameter is thetime from the activation of a vacuum solenoid to the presence of a sheetof paper at the solenoid. The state machine can calculate the time bysubtracting the time tag of the entry that caused the transition tostate S2 from the time tag of the entry that caused the transition tostate S4. The state machine can output the time, e.g., in milliseconds,and a text string or code indicating that the time represents solenoidactivation to detection of paper. After generating the output, the statemachine returns to state S1 to wait for the next starting event.

As stated above, the parser preferably executes a plurality of statemachines for analyzing time-tagged data to identify multiple events andmeasure multiple parameters associated with the industrial process. FIG.4 illustrates another state machine that can be executed by the parseraccording to an embodiment of the present invention. The state machineillustrated in FIG. 4 detects overlapped sheets of paper in the entryarea of a turnover sequencer.

The following event list includes events that result in statetransitions in the state machine illustrated in FIG. 4:

Event List

-   -   A) Inside page lead edge detected;    -   B) Outside page lead edge detected;    -   C) Inside turnover lead edge detected; and    -   D) Outside turnover lead edge detected.        In a turnover sequencer (“TOS”), sheets of paper enter        side-by-side and exit in an single overlapped stream. Thus, in        the event list, event A corresponds to the detection of the        leading edge of the inside sheet and event B corresponds to the        detection of the leading edge of the outside sheet. Event C        corresponds to the detection of the lead edge of the inside        sheet at the entry portion of the turnover area of the turnover        sequencer. Event D corresponds to the detection of the leading        edge of the outside sheet at the entry portion of the turnover        area of the turnover sequencer. Overlapped sheets are identified        when lead edges of the outside and inside pages are detected by        the entry photocells of the TOS before either enters the        turnover area.

In state S1, “Waiting for Paper”, the parser determines whether thecurrent log file entry corresponds to event A, “Inside page lead edgedetected” or event B, “Outside page lead edge detected”. Exemplary logfile entries corresponding to events A and B are as follows:

Event A: Inside Page Lead Edge Detected

-   0010.02588000.00156000.324750 RTENICEL-BIN_MUX_IN    SET:board=1,port=06,pin=001

Event B: Outside Lead Edge Detected

-   0009.995850 00.00093000.125760 RTENOCEL-BIN_MUX_IN    SET:board=1,port=06,pin=001

In state S1, if event A occurs, the state machine transitions to stateB, “Inside Page Seen”. The parser preferably also executes any remainingstate machines relevant to the current log file entry. The parser thenreads the next log file entry and applies conditions relevant to stateS2. In state S2, the parser determines whether the current log fileentry corresponds to event B or event C, “Inside turnover lead edgedetected”. An exemplary time-tagged data entry corresponding to event Cis as follows:

Event C: Inside Turnover Lead Edge Detected

-   0011.705250 00.002970 00.097980 RTTUICEL-BIN_MUX_IN    SET:board=1,port=09,pin=001

If event C is detected, the state machine returns to state S1. If eventB occurs when the parser is in state S2, the state machine transitionsto state S3, “Two pages in Turnover Area”.

In state S3, the parser generates output indicating that two pages haveentered the turnover area. When the parser has completed generating theoutput, the state machine automatically returns to state S1 to wait forthe next sheet of paper. When analyzing a log file, waiting for the nextsheet of paper can include searching through remaining log file entriesuntil another entry corresponding to event A or B is located.

When the state machine is in state S1 and event B is detected for thecurrent log file entry, the state machine enters state S4, “Outside PageSeen”. In state S4, the parser analyzes the current log file entry todetermine the presence of text indicating event A or event D, “Outsideturnover lead edge detected”. An example of a log file entrycorresponding to event D is as follows:

Event D: Outside Turnover Lead Edge Detected

-   0011.824260 00.012030 00.278430 RTTUOCEL-BIN_MUX_IN    SET:board=1,port=09,pin=001

In state S4, if the current entry corresponds to event A, the parserenters state S3, generates the output indicating the presence of twooverlapped pages, and returns to state S1. If the current entrycorresponds to event D, the parser returns to state S1.

The state diagram illustrated in FIG. 4 illustrates that events resultin different transitions, depending on the current state. In terms oflog file entries, this means that a given log file entry can have adifferent meaning, depending on previous log file entries. A humanobserver scanning a log file would be required to remember previous logfile entries to determine the significance of current log file entries.By applying state machines to each log file entry, the parser accordingto the present embodiment “remembers” previous information in order todetermine the significance of present information. Accordingly, theburden on the user in interpreting log file data is significantlyreduced. This advantage applies equally to time-tagged data that isrecovered and processed in real time.

The methods and systems for analyzing time-tagged data are not limitedto applying the state machines in FIGS. 3 and 4 to time-tagged data.These state machines are merely intended to illustrate, withoutlimitation, examples of how state machines can be applied to identifyreal-world events and measure parameters from time-tagged data. Anysuitable state machines that identify real world parameters or eventsfrom time-tagged data are within the scope of the present invention.

FIG. 5 illustrates an exemplary paper processing system in which thepresent invention can be used to analyze time-tagged data. The systemincludes a cutter 100, a hold area 102, a TOS 104, an accumulator 106,and a collector 110. The operation of each of these devices and examplesof how state machines can be applied to analyze time-tagged data outputfrom each of the devices will now be explained.

In the illustrated system, cutter 100 cuts pages at selected locations,e.g., at the boundaries of every page, slits pages that are printed in2-up applications, and removes any trim (i.e., tractor pin holes alongthe edges of a page). State machines can be applied to time-tagged datacollected from this device to:

-   -   1) Monitor the rate, e.g., in cuts per hour, at which cuts are        performed; and    -   2) Monitor the delay, e.g., in milliseconds, between starting        each cut and when read data is available.

Hold area 102 holds pages after the pages are cut and before the pagesenter other parts of the system. Hold area 102 is used to adjust thetiming relationship between two pages that are side-by-side in the caseof a 2-up application. State machines can be applied to time-tagged dataoutput from this device to:

-   -   1) Monitor the speed e.g., in inches per second, at which paper        enters hold area 102;    -   2) Monitor the speed, e.g., in inches per second, at which paper        leaves hold area 102;    -   3) Monitor the amount of time, e.g., in milliseconds, that pages        stay in hold area 102;    -   4) Measure paper skew, e.g., in milliseconds, at entry to hold        area 102; and    -   5) Measure difference, e.g., in milliseconds, between left and        right pages leaving hold area 102 for 2-up applications.

TOS 104 moves two pages through a right-angle turn, converting the pagesfrom two side by side streams to a single stream in which the pages mayor may not overlap. Pages that move through a TOS are flipped over inthe turnover process. State machines can be applied to time-tagged dataoutput from this device to:

-   -   1) Monitor the speed, e.g., in inches per second, at which paper        enters TOS 104;    -   2) Measure difference, e.g., in inches, between left and right        pages entering TOS 104 for 2-up applications;    -   3) Detect single and overlapped pages and report to other state        machines via flares, which are discussed in more detail below;    -   4) Measure the amount of overlap, e.g., in inches, at various        sensors and monitor how the amount of overlap varies as a page        moves through the rest of the machine;    -   5) Monitor the speed, e.g., in inches per second, at which paper        leaves TOS 104; and    -   6) Monitor the paper speed, e.g., in inches per second, at        sensors internal to TOS 104.

Accumulator 106 combines and stacks single and overlapped pages intomultiple-page sets. Set sizes range from one page up to the maximumallowed by the mechanical limits of folder 108. Each set will beinserted into one envelope, possibly with other sets destined for thesame customer. State machines can be applied to data output from thisdevice to:

-   -   1 ) Monitor the speed, e.g., in inches per second, at which        paper enters accumulator 106;    -   2) Monitor the speed, e.g., in inches per second, at which paper        leaves accumulator 106;    -   3) Monitor the amount of time, e.g., in milliseconds, that pages        stay in the area of accumulator 106;    -   4) Count the number of pages in each set and report to other        state machines via flares;    -   5) Monitor time delay, e.g., in milliseconds, between releasing        of a brake and detection of paper at the exit of accumulator        106; and    -   6) Monitor and compare times for pages in the upper deck of        accumulator 106 versus the lower deck of accumulator 106.

Folder 108 folds sets in a predetermined pattern. State machines can beapplied to time-tagged data output from this device to:

-   -   1) Monitor the speed, e.g., in inches per second, at which paper        enters folder 108;    -   2) Monitor the speed, e.g., in inches per second, at which paper        leaves folder 108;    -   3) Monitor differences in speed, e.g., in inches per second, for        different set sizes; and    -   4) Monitor differences in speed, e.g., in inches per second, for        sets from upper vs. lower decks of accumulator 106.

Collector 110 provides an area where folded sets can be combined (orcollected) with other folded sets destined for the same customer, whichis known as sub-set collection. State machines can be applied totime-tagged data output from this device to:

-   -   1) Monitor the speed, e.g., in inches per second, at which paper        enters collector 110;    -   2) Monitor set distributions, e.g., in pages per set;    -   3) Monitor behavior differences between sets from upper and        lower decks of accumulator 106; and    -   4) Monitor speed differences for heavy vs. light sets.

Another function of collector 110 is to provide a two-stage holding areato allow one area of an inserter (not shown) to synchronize sets passedto the next area of the inserter. This small buffer area decouplesoperation of these two machine parts and allows productivity to bemaintained when large and small sets are mixed. State machines can beapplied to time-tagged data relating to set synchronization output fromthis device to:

-   -   1) Monitor the speed, e.g., in inches per second, at which paper        leaves collector 110;    -   2) Monitor speed differences, e.g., in inches per second, for        heavy vs. light sets; and    -   3) Monitor the rate, e.g., in sets per hour, at which sets leave        collector 110.

Yet another function of collector 110 is to provide the ability todivert (or remove) sets containing errors from the processing stream,such as to a divert area, without requiring operator intervention ormachine stoppage. State machines can be applied to time-tagged datarelating to this device function to:

-   -   1) Monitor the speed, e.g., in inches per second, at which        diverted sets move through the divert area;    -   2) Count the number of diverted sets, e.g., in diverts per hour;        and    -   3) Identify patterns in reasons, such as bad read, cover opened,        too many pages, for diverting sets.

The present invention is not limited to applying state machines totime-tagged data from the paper processing machines illustrated in FIG.5. For example, additional paper processing machines to which themethods and systems according to the present invention can be appliedinclude sheet feeders, bursters, readers, and other paper processingequipment. A sheet feeder is a paper processing device that provides astream of single sheets from a stack of cut sheets. A sheet feeder isused in place of a cutter or a burster when a customer decides to printindividual sheets instead of rolled or fan folded paper. State machinescan be applied to time-tagged data output from a sheet feeder to:

-   -   1) Monitor the rate, e.g., in sheets per hour, at which sheets        are fed;    -   2) Monitor the delay, e.g., in milliseconds, between starting        each page and when read data is available; and    -   3) Monitor the delay, e.g., in milliseconds, between command to        start feeding and detection of paper movement.

As recognized by those of skill in the art, a burster is a paperprocessing device that provides a stream of single sheets from paperthat has been horizontally perforated at page boundaries. A burster canalso convert 2-up printing into a singulated stream of sheets and willremove any trim (i.e., tractor pin holes along the edges of page). Statemachines can be applied to time-tagged data output from a burster to:

-   -   1) Monitor the rate, e.g., in sheets per hour, at which sheets        leave the burster;    -   2) Monitor the gap, e.g., in milliseconds, between sheets; and    -   3) Monitor the speed, e.g., in inches per second, at which        sheets leave the burster.

As stated above, the methods and systems for analyzing time-tagged dataaccording to the present invention can be used to analyze time taggeddata output from a reader. A reader is a paper processing device thatprovides an area where printing on the paper representing processinginstructions are obtained for each sheet that enters the machine. Thereader can be separate from or combined with a burster. A reader mayalso be combined with or separate from cutters and sheet feeders. Statemachines can be applied to time-tagged data output from a reader to:

-   -   1) Monitor the rate, e.g., in sheets per hour, at which sheets        enter the reader;    -   2) Monitor the speed, e.g., in sheets per hour, at which sheets        enter the reader;    -   3) Monitor the speed, e.g., in inches per second, at which        sheets leave the reader;    -   4) Monitor the gap, e.g., in milliseconds, between sheets at the        entry of the reader;    -   5) Monitor the gap, e.g., in milliseconds, between sheets at        exit of the reader; and    -   6) Monitor the number or count of sheets in the reader at any        time.

From the above-listed devices it is apparent that in paper processing, aplurality of machines act in concert in performing a specified function,such as stuffing an envelope. This results in large quantities oftime-tagged data entries that are interspersed with each other. Manuallyanalyzing such data is impractical for untrained personnel and timeconsuming for personnel trained to analyze such data. However, becausethe methods and systems for analyzing time-tagged data apply localizedstate machines to the time-tagged data, real-world events and parametersassociated with each device can be easily identified.

In the examples described above, parameters are measured in inches persecond and milliseconds. However, the present invention is not limitedto measuring parameters in milliseconds or inches per second. Forexample, other units for which parameters can be calculated includeinches, mils ( 1/1000 of an inch), cycles per hour, and percentages,e.g., percentages of data points falling within a specified value range.

The present invention is not limited to methods and systems foranalyzing time-tagged data for the devices described above. For example,additional devices for which the methods and systems according to thepresent invention may be used to analyze time-tagged data includesorters and inspection devices. Analyzing time-tagged data from any mailor paper device is within the scope of the invention.

Flares

According to an important aspect of the invention, state machinespreferably share data with other state machines. More particularly, whenone aid state machine produces data needed by another state machine, thefirst state machine preferably communicates the data to the second statemachine. This inter-state-machine communication is referred to herein asa “flare”.

For example, suppose state machine A executes and detects the occurrenceof an event relevant to the execution of state machine B. Rather thanre-executing the steps required for detecting the occurrence of theevent, state machine B preferably uses the event produced by statemachine A. In order to communicate the occurrence of the event to statemachine B, state machine A can write data indicating the occurrence ofthe event in a memory location accessible by the process executing statemachine B. The process executing state machine B can read the memorylocation containing the data. As a result, the steps required fordetecting the occurrence of the event are preferably executed only once.

In addition to communicating data indicating the occurrence of an eventbetween state machines, the parser can also communicate the time ofoccurrence of the event between state machines. However, communicatingthe time of occurrence of an event is not a required feature of theinvention. For example, if the event detected by state machine A is “Aman walked through the door” or “A woman walked through the door”, thisdata can be communicated to state machine B without communicating thetime of the event to state machine B. Alternatively, state machine Acould communicate the time that the man or woman walked through the doorto state machine B. However, if it is desirable to report a time alongwith the occurrence of an event, it is also preferable that the time bedefined with regard to the event. For example, in the example discussedabove, the time that the man or woman entered the door or completedwalking through the door can be reported. Any suitable method ofdefining and communicating time between state machines is within thescope of the invention.

Using flares to communicate the occurrence of events between statemachines greatly reduces software complexity as will be readilyappreciated by those of skill in the art. As a result, programsimplementing the state machines might execute more quickly. Flares alsoaid in the overall design of the parser. For instance, in paperprocessing, if a single state machine is designed to monitor an area ofthe machine where more than one page could pass through before thatstate machine produces its output (or not) for the first page, thecomplexity of the overall design of the parser can be increased. Flaresallow the localization of state machine operation to a small area of adevice, such as the turnover area of a TOS and/or related processingequipment. This localization greatly simplifies the design of each statemachine that needs to span several machine areas to produce a certainoutput. Thus, flares can be used to provide an indication of an eventwhich occurred elsewhere in the machine being maintained to modify thebehavior of a state machine that relates to processes further downstreamin the system being monitored.

One example in which flares can be useful in a paper processingenvironment specifically is to communicate the presence of overlappedpages in a Turnover Sequencer (TOS). In paper processing, the TOSchanges paper motion at the exit into one at right angles to papermotion at the entry. This change in paper motion results in a rightangle turn. The TOS is constructed to simultaneously receive two pagesside-by-side (2-up) and turn them over so that the pages exit the devicein a single stream. The page on the outside of the turn takes longer topass through the TOS and therefore experiences a natural delay becauseit has to travel slightly farther than the inside page. This action“sequences” the pages so that the page that will come out first can beidentified. This action also overlaps the pages so that two pages cantravel through the paper processing machines in the same space toimprove productivity. When overlapping pages in this manner, it isundesirable for the pages to pull apart, because the order in which thepages will overlap at the next stage in the paper processing sequencemight not be easily determinable. Therefore, it is desirable to monitorthe amount of overlap for such page groups while ignoring single pages(which are not overlapped) that pass through the TOS.

A TOS can include four photocells over which overlapped and singlesheets pass. Each of the photocells can be used to measure the amount ofoverlap. Overlap detection is preferably started at the entry to the TOSwhen pages are still side-by-side. This is the principle on which thestate diagram illustrated in FIG. 4 is based. From the design of theTOS, i.e., the length between the entry photocells and the turnoverarea, if two lead edges cross their respective entry photocells beforeeither enters the turnover area, the pages will overlap. It might not beknown which of the sheets will enter the turnover area first, as thisdepends on the specific shape of the paper being processed. However, ifboth sheets are present in the turnover area without an exit, it can beassumed that an overlapped set coming is traveling through the TOS.

The state machine illustrated in FIG. 4 can be used to detect thepresence of overlapped pages. For example, the state machine can monitorthe entry photocells of the TOS for lead edges. The state machine isstructured so that either sheet, i.e., the inside sheet or the outsidesheet, can enter the TOS first. For example, Events A and B in FIG. 4are parallel paths to states S4 and S2, depending on whether the insidepage or the outside page is detected first. In state S2 or state S4, onepage has detected. If another page is detected at the TOS entry, thestate machine transitions to state S4, which indicates that two pagesare in the turnover area. In state S4, it can be assumed that the pagesthat entered the TOS are overlapped.

Because the detection of overlapped pages is an important event that canbe used by downstream devices, the state machine illustrated in FIG. 4preferably sends a flare to all other state machines. The flarepreferably includes a unique name. Another state machine, for example,the state machine analyzing the data collected by the first overlap cellcan be watching for that name. When the state machine analyzing datacollected by the first overlap cell receives the flare, it classifiesthe page as an overlapped set and reports it as such. If the flare isnot received, the state machine analyzing data collected by the firstoverlap cell assumes that the page currently detected by the statemachine is only a single page. Once the state machine analyzing datacollected by the first overlap cell then detects a page, the statemachine waits until the trail edge of the overlapped pages is detectedand “throws” another flare with a unique name that indicates anoverlapped page is leaving the first turn in the TOS. That flare is“caught by” a state machine that monitors the next photocell downstreamand treats the detection of pages overlapped pages. This process isrepeated for all state machines for which overlapped pages are relevant.

In paper processing, a key observation to remember is that paper might(or might not) simultaneously exist under many of these photocells. Anycombination of pages could be overlapped at any time. The flaresindicate when a page is overlapped and the unique name of each flareallows the state machines to keep track of multiple sets of overlappedpages. If a flare is thrown and a state machine does not require theinformation provided by the flare, the flare is ignored.

In addition to communicating information to downstream state machinesthat can be discovered by upstream state machines, such as the presenceof overlapping pages, flares also pass information to downstream statemachines that the downstream state machines are unable to discoverwithout flares. For example, flares can be used to pass paperattributes, such as the number of pages in a set, previous path taken bythe set, or processing applied to the set, along with the paper to whichthe attributes apply.

For purposes of tracking overlapped pages, it can be necessary to knowboth the average time for a single page to pass a photocell, as well asthe time for each overlapped page to pass the photocell. With flares,these two modes e of operation can be identified and tracked separately.In a preferred embodiment of the present invention, a Microsoft EXCEL®spreadsheet performs the calculations necessary to determine the amountof overlap e.g., in inches or centimeters, for each page that is seen.If the average overlaps at each of the photocells of interest in the TOSare analyzed, it is apparent that the average overlap decreases at eachsuccessive stage in the TOS. This is an effect of how the machine runsand is expected. If paper processing begins with an overlap that is toosmall, the pages will separate and cause sequencing problems. If therange of these values (max-min) is large, this can indicate errors inpaper motion control software and other problems, such as insufficientdrive pressure on paper. If the gap between lead edges varies at thestart of the process, the amount of overlap variation increases as thepages proceed through the paper processing system. This variation inoverlap has been observed experimentally and utilized to modify paperprocessing control software. An analysis tool, such as a system foranalyzing time-tagged data according to the present invention, couldhave been used to detect this issue before releasing the paperprocessing control software. Thus, the methods and systems for analyzingtime-tagged data according to the invention are useful in validatingpaper processing control software.

Statistical Analysis. Application of Design Limits, and OutputGeneration

Referring back to FIGS. 1 and 2, after the state machines are applied tothe time-tagged data, statistical measures are computed for the outputproduced by the state machines, design limits are applied to thestatistical measures, and the output is displayed to a user. Exemplarystatistical measures that can be computed for the data output from thestate machines include minimum values, maximum values, range values,mean values, median values, standard deviation values, mode values andvariance values. For example, it can be desirable to know the speed atwhich paper passes a given sensor in paper processing. In order todetermine the speed, it is necessary to know the page length anddetermine the amount of time for each page to pass by a photocell in themachine being monitored. The page length can be entered by the operatoror extracted from the time-tagged data. In order to avoid operatorerrors in entering the page length, it is preferable that the pagelength be included in the time-tagged data being tested. The amount oftime for a page to pass the sensor is measured by a state machine thatfinds the lead edge of the page, records the start time for thedetection of the lead edge, finds the trail edge of the same page, andrecords an end time for the detection of the trail edge. The transittime is calculated by subtracting the start time from the end time. Thestate machine produces a vector of numbers for a given batch of papersbeing processed indicating the transit times for a given photocell.

The final item required to be considered in computing speed is thelength of sensitive area under the photocell. Photocells do not have aninfinitesimally small point where page edges are detected. Photocellshave a “hot spot” where a page anywhere in that area will be detected.For photocells commonly used in paper processing, this area is typicallyabout 0.2 inches for each cell, when measured empirically. The hot spotfor each photocell increases the apparent page length by the length ofthe hot spot. Thus, when computing the speed, the length of the hot spotis added to each page length. This value is then divided by the transittime for a page. The quotient is the speed in inches per second, orother appropriate unit, for each page. The speed values for the pagesbeing processed are stored as a vector of numbers, e.g., in aspreadsheet. From this vector, statistical measures, such as minimum,maximum, average, and standard deviation are calculated. This data canalso be used to generate graphs, such as histograms, indicating trendsin machine parameters.

FIG. 6( a) is an example of output that is generated from time-taggeddata according to an embodiment of the present invention. The outputcomprises a graphical interface displayable on a computer display devicethat allows the user to view data in various formats. For example, inFIG. 6( a), a first window 600 displays a plurality of process parameterdescriptions, such as “sheet feeder entry cell transit time”, thatindicate parameter values of interest calculated from time-tagged data.Window 600 also includes status indicators that indicate whether theparameters are within, near, or outside of design tolerances. In theillustrated embodiment, text messages, “OK”, “Warn”, and “Error”, arecombined with familiar colors, green, yellow, and red, to indicate tothe user whether a process is operating within design limits. If aparameter is well within design limits, “OK” is displayed in a greenbox. If a parameter is close to, e.g., the average is within threestandard deviations, of a design limit, “Warn” is displayed in a yellowbox. If a parameter is outside of design limits, “Error” is displayed ina red box.

As an example of how design limits can be applied to statisticalmeasures, a design specification for an area can require that papershould run at 120 IPS, +/−5%. This results in-a lower and upper limit of114 IPS and 126 IPS, respectively. When time-tagged data is analyzed forthe machine, it can be determined that paper actually runs at an averageof 122.2 IPS with upper and lower measured values of 116 IPS and 123 IPSand a standard deviation of 1.5. Since both the upper and lower measuredvalues are within the design limits, a RED error message is notdisplayed. Warnings are then checked. A warning can be produced if theaverage is within +/−3 standard deviations of a limit. In this examplethe average of 122.2 plus three standard deviations is 126.7 (above theupper limit), so a warning can be indicated to the user. The averageminus three standard deviations is 117.7, which is not below the lowerlimit, so no warning is produced for the lower design limit.

The present invention is not limited to using text and colors to informthe user of the status of a particular process parameter. Any suitablemethod can be used to communicate the status to a user in accordancewith this invention. For example, in an alternative embodiment of theinvention, an audible alarm can be used to indicate that a processparameter is outside of an acceptable range.

When the user selects one of the parameter descriptions in window 600,statistical information is displayed for that parameter description inwindow 602 and a histogram is displayed for that parameter in window604. In the illustrated embodiment, “Sheet Feeder entry cell transittime” is selected in window 600. Accordingly, window 602 displaysstatistical data computed from measured and reference sheet feeder entrycell transit time data. Minimum, maximum, average, standard deviation,and count values are displayed in inches per second. Window 602 alsoincludes a dimensions button 606 and a print report button 608 thatallow the user to specify dimensions of paper or envelopes beingprocessed and print a report of the statistical calculations for all ofthe parameters displayed in window 600.

In addition to displaying measured data in the window 604, referencedata is also displayed in the window 604. The reference data can bemeasured data from previous paper processing operations. Simultaneouslydisplaying the data produced from analyzing the time-tagged data withthe reference data greatly facilitates interpretation of the time-taggeddata. The user can visually determine the difference between themeasured data and the reference data simply by viewing the graphs in thewindow 604. As a result, the time and labor required to identifyprocessing problems from time-tagged data is reduced.

In addition to displaying statistical data for measured values in thewindow 604, reference data is preferably also displayed in the window602. For example, in the illustrated embodiment, reference statisticalvalues are displayed for each measured statistical value. Displayingreference statistical values in the window 604 further facilitates userinterpretation of time-tagged data.

When the user selects dimensions button 606, a dimensions dialog box,generally designated DDB, appears and allows the user to enterinformation that is not present in the time-tagged data, such as paperor envelope dimensions, dimensions of paper of envelopes beingprocessed. An example of dimensions dialog box DDB is illustrated inFIG. 6( b). Dimensions dialog box DDB illustrated in FIG. 6( b) permitsthe user to enter the following dimensions:

-   -   1) page length;    -   2) folded page length;    -   3) envelope width; and    -   4) envelope height;        for both reference and measured data. This allows the user to        compare operation of the machine when running jobs with        different sizes of paper. The output line of each state machine        has an embedded string that determines the proper units of        measurement for class of data along with any design limits to        use.

Because calculation of transit speed, e.g., in inches per second,depends on page or envelope dimensions. when the user changes the pageor envelope dimensions using dimensions dialog box DDB illustrated inFIG. 6( b), the calculated transit speed changes. The specifieddimensions should match those for the process for which the time-taggeddata was collected. Otherwise, speed data can be incorrect and lead tofalse measurements. In order to avoid this potential problem, as statedabove, the page dimensions can be included in the time-tagged data. Inan embodiment in which dimensions are included in the time-tagged data,the dimensions dialog box DDB may be omitted.

Referring back to FIG. 6( a), when the user presses print report button608, a process information dialog box appears for receiving informationregarding the operator, machine, and additional information. FIG. 6( c)illustrates an example of a process information dialog box, generallydesignated PIDB, that can be displayed. In the illustrated embodiment,process information dialog box PIDB includes an input cell for “OperatorName” to allow the operator to enter his or her name. A second inputcell labeled “Machine ID” allows the user to enter the identificationnumber of the machine being tested. A third input cell labeled“Additional Info” allows the user to specify any additional information,such as data relating to the test being run on a machine. Processinformation dialog box PIDB also allows the user to select whether todisplay the report on the screen before printing. Once the user entersthe process information dialog box illustrated in FIG. 6( c) and selects“Preview on Screen” the user clicks on “OK”. A report is displayedindicating calculated statistical values for each of the parametersdisplayed in the window 600.

FIG. 6( d) illustrates an example of a report that may be displayed. Inthe illustrated embodiment, the operator name, machine identifier, andadditional information entered into the process information dialog boxillustrated in FIG. 6( c) are displayed on the upper portion of thereport. In FIG. 6( d), the statistical measures calculated from thetime-tagged data are displayed in tabular format. In the column labeled“Item Description”, names of each of the parameters from the window 600are displayed. The column labeled “Minimum” displays minimum measuredvalues for each of the parameters. The column labeled “Average” displaysaverage values for each of the parameters. The column labeled “Maximum”displays maximum values for each of the parameters. The column labeled“Standard Deviation” displays standard deviation values for each of theparameters. Finally, the column labeled “Status” displays the status ofeach of the parameters, i.e., whether the parameter is within, near, oroutside of design limits. A Warnings and Errors List is displayed tosummarize parameters for which warnings or errors were detected. TheWarning and Errors list preferably also indicates the design limit thatwas exceeded, e.g., the minimum, the maximum, or average value for aparameter.

Referring back to FIG. 6( a), graph window 604 displays a histogram ofsheet feeder entry cell transit time. In the histogram, the horizontalaxis represents sheet feeder entry cell transit time in inches persecond. The vertical axis represents the percentage of values for eachtransit time. The bars represent sheet feeder entry cell transit timescalculated based on time-tagged data, as discussed above. The linedportion of the graph represents reference values, which may be valuesextracted and calculated from time-tagged data for the same machine atanother time or from another machine.

The graphical interfaces for displaying the results of analyzingtime-tagged data can be contrasted with the raw time-tagged data entrieslisted above. Presenting statistical data indicative of real-worldevents and measurements in an industrial process greatly facilitatesinterpretation of the time-tagged data. The presentation of data inFIGS. 6( a)–6(d) allows the user to immediately determine whether amachine is operating within design limits, while the raw time-taggeddata entries stored in the log file may require hours of manual analysisbefore such a determination can be made.

Methods and Systems for Analyzing Non-Time-Tagged Data

Although the embodiments of the invention described above illustratemethods and systems for analyzing time-tagged data, the presentinvention is not limited to such embodiments. For example, in analternative embodiment of the invention, data from any of the mail orpaper processing operations described above may be collected,statistically analyzed, and presented to a user in a manner thatfacilitates user interpretation of the data, for example, as illustratedin FIGS. 6( a) and 6(d). The data analyzed might or might not have timetags. For example, mail or paper processing control software may outputmeasured data, such as inches of paper passing a given sensor persecond. The paper processing control software may determine the paperspeed in inches per second based on knowledge of the length of eachsheet and the time required for sheets to pass a given sensor. Forexample, the length of each sheet may be input into the paper processingcontrol software and the time may be measured electronically. The lengthdivided by the time equals the speed.

In the case where the paper processing control software outputs speed ininches per second, state machines would not be necessary to calculatethis data. Thus, referring back to FIG. 1, in the present embodiment,parsing time-tagged data (step ST1) may be replaced by the steps oflocating log file entries containing measurements for a given parameteror a given sensor and recording the measured values. Steps ST2–ST4 wouldbe the same as steps ST2–ST4 described above for time-tagged data,whereby statistical measures are computed, compared to reference values,and displayed to a user. Thus, the methods and systems for analyzingtime-tagged data according to the invention are equally applicable toanalyzing non-time-tagged data associated with an industrial process.

It is therefore seen that the present invention provides a novelautomated method and system for analyzing time-tagged data and, moregenerally, any data associated with an industrial process. As can beappreciated by those of skill in the art, it can also be seen that thepresent invention provides methods and systems for analyzing dataassociated with an industrial process to produce output that facilitatesinterpretation of the data.

It will be understood that various details of the invention can bechanged without departing from the scope of the invention. Furthermore,the foregoing description is for the purpose of illustration only, andnot for the purpose of limitation, as the invention is defined by thefollowing, appended claims.

1. In a computer system having a graphical user interface including adisplay and a user input device, a method for displaying statisticalmeasures for selected parameter values produced from analysis oftime-tagged data from a mail or paper processing system, the methodcomprising: (a) analyzing time-tagged data associated with a pluralityof machines of different types associated with a mail or paperprocessing system for producing parameter values of the mail or paperprocessing system, wherein analyzing the time-tagged data comprises: (i)reading a plurality of time-tagged data items received from theplurality of machines, wherein the reading includes accessing a log filecontaining the time-tagged data items, the time-tagged data itemsassociated with an event of interest for a particular machine spanningmultiple, separated entries in the log file; and (ii) parsing thetime-tagged data items to identify multiple, separated data itemsrelating to at least one event of interest for a particular machineassociated with the mail or paper processing system; (b) displaying, onthe display, a first window including parameter descriptions for mail orpaper processing parameter values produced from the analysis oftime-tagged data, and including status information indicating theresults of comparing the parameter values to reference values; (c)displaying, on the display, a second window including a table ofstatistical measures for a selected parameter description produced fromthe analysis of time-tagged data in the first window; (d) displaying, onthe display, a third window including a graph of measured values for theselected parameter description; and (e) receiving input from a user forselecting the parameter description.
 2. The method of claim 1, whereinthe graph is a histogram of measured values for the selected parameterdescription.
 3. The method of claim 1 wherein the graph is a histogramof measured values and references values for the selected parameterdescription.
 4. The method of claim 1 comprising receiving input fromthe user for printing a report including the statistical measures forthe selected parameter description.
 5. In a computer system having agraphical user interface including a display and a user input device, amethod for displaying statistical measures for selected parametersproduced from analysis of time-tagged data from a mail or paperprocessing system, the method comprising: (a) analyzing time-tagged dataassociated with a plurality of machines of different types associatedwith the mail or paper processing system for producing parameter valuesof the mail or paper processing system, wherein analyzing thetime-tagged data comprises: (i) reading a plurality of time-tagged dataitems received from the plurality of machines, wherein the readingincludes accessing a log file containing the time-tapped data items, thetime-tapped data items associated with an event of interest for aparticular machine spanning multiple, separated entries in the lop file;and (ii) parsing the time-tagged data items to identify multiple,separated data items relating to an event of interest for a particularmachine associated with the mail or paper processing system; (b)displaying, on the display, a first window including parameterdescriptions for mail or paper processing values produced from theanalysis of time-tagged data, and including status informationindicating results of comparing the parameter values to referencevalues; (c) displaying, on the display, a second window including atable of statistical measures for a selected parameter descriptionproduced from the analysis of time-tagged data in the first window; (d)displaying, on the display, a third window including a graph of measuredvalues for the selected parameter description; and (e) receiving inputfrom the user for selecting the parameter description, and in responseto receiving the input from the user, displaying, in the second window,a table of statistical measures for the selected parameter descriptionand displaying, in the third window, a graph of measured values for theselected parameter description.
 6. The method of claim 5 comprisingsimultaneously displaying, in the second window, reference statisticalvalues for the selected parameter description and statistical measuresfor the selected parameter description.
 7. In a computer system having agraphical user interface including a display, a method for displayingstatistical measures for selected parameter values produced fromanalysis of time-tagged data from a mail or paper processing system, themethod comprising: (a) analyzing time-tagged data associated with aplurality of machines of different types associated with the mail orpaper processing system for producing parameter values of the mail orpaper processing system, wherein analyzing the time-tagged datacomprises: (i) reading a plurality of time-tagged data items receivedfrom the plurality of machines, wherein the reading includes accessing alog file containing the time-tagged data items, the time-tagged dataitems associated with an event of interest for a particular machinespanning multiple, separated entries in the log file; and (ii) parsingthe time-tagged data items to identify multiple, separated data itemsrelating to at least one event of interest for a particular machine andto measure a parameter of interest associated with the mail or paperprocessing system; (b) displaying on the display parameter descriptionsfor mail or paper processing parameter values produced from the analysisof time-tagged data, and including status information indicating resultsof comparing the parameter values to reference values; (c) displaying onthe display statistical measures for a selected parameter descriptionproduced from the analysis of time-tagged data; (d) receiving input froma user for selecting the parameter description: (e) displaying a graphof measured values for the selected parameter description: and (f)displaying a table of statistical measures for the selected parameterdescription.
 8. The method of claim 7, wherein analyzing the time-taggeddata comprises generating statistical measure data by computingstatistical measures of the parameter measured.
 9. The method of claim8, wherein analyzing the time-tagged data comprises applying limits tothe statistical measures data to identify whether the machines areoperating within predetermined tolerances.